AMR/index.md

The AMR Package for R

• Provides an all-in-one solution for antimicrobial resistance (AMR) data analysis in a One Health approach
• Peer-reviewed, used in over 175 countries, cited over 100 times, available in 28 languages
• Generates antibiograms - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
• Provides the full microbiological taxonomy of ~103 000 distinct species and extensive info of ~620 antimicrobial drugs
• Applies CLSI 2011-2026 and EUCAST 2011-2026 clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
• Corrects for duplicate isolates, calculates and predicts AMR per antimicrobial class
• Integrates with WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, and NCBI
• 100% free of costs and dependencies, highly suitable for places with limited resources

Available for Python too! Click here to read more.

amr-for-r.org

doi.org/10.18637/jss.v104.i03

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Introduction

The AMR package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.

Our aim has always been to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of many different researchers from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research.

After installing this package, R knows ~103 000 distinct microbial species (updated april 2026) and all ~620 antimicrobial and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen and the University Medical Center Groningen.

Used in over 175 countries, available in 28 languages

Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.

With the help of contributors from all corners of the world, the AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.

Practical examples

Filtering and selecting data

One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called antimicrobial selectors, which work in base R, dplyr and data.table.

# AMR works great with dplyr, but it's not required or neccesary
library(AMR)
library(dplyr, warn.conflicts = FALSE)

example_isolates %>%
  mutate(bacteria = mo_fullname()) %>%
  # filtering functions for microorganisms:
  filter(mo_is_gram_negative(),
         mo_is_intrinsic_resistant(ab = "cefotax")) %>%
  # antimicrobial selectors:
  select(bacteria,
         aminoglycosides(),
         carbapenems())
#> ℹ Using column mo as input for `mo_fullname()`
#> ℹ Using column mo as input for `mo_is_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023).
#>   This note will be shown once per session.
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN
#>   (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # A tibble: 35 × 7
#>    bacteria                     GEN   TOB   AMK   KAN   IPM   MEM  
#>    <chr>                        <sir> <sir> <sir> <sir> <sir> <sir>
#>  1 Pseudomonas aeruginosa       I     S     NA    R     S     NA   
#>  2 Pseudomonas aeruginosa       I     S     NA    R     S     NA   
#>  3 Pseudomonas aeruginosa       I     S     NA    R     S     NA   
#>  4 Pseudomonas aeruginosa       S     S     S     R     NA    S    
#>  5 Pseudomonas aeruginosa       S     S     S     R     S     S    
#>  6 Pseudomonas aeruginosa       S     S     S     R     S     S    
#>  7 Stenotrophomonas maltophilia R     R     R     R     R     R    
#>  8 Pseudomonas aeruginosa       S     S     S     R     NA    S    
#>  9 Pseudomonas aeruginosa       S     S     S     R     NA    S    
#> 10 Pseudomonas aeruginosa       S     S     S     R     S     S    
#> # ℹ 25 more rows

With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (mo_is_gram_negative() and mo_is_intrinsic_resistant()) and a column selection on two antibiotic groups (aminoglycosides() and carbapenems()), the reference data about all microorganisms and all antimicrobials in the AMR package make sure you get what you meant.

Generating antibiograms

The AMR package supports four types of antibiograms, with support for 28 languages. If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).

For empirical therapy guidance (i.e., coverage estimates), use WISCA (Weighted-Incidence Syndromic Combination Antibiogram). When a clinician starts empirical treatment, the causative pathogen is unknown. The relevant question is not “what percentage of E. coli is susceptible?” but “what is the probability that this regimen will cover whatever pathogen is causing the infection?”. WISCA answers that question directly, weighting susceptibility by pathogen incidence and providing credible intervals via Bayesian simulation. See vignette("WISCA") for the full explanation.

wisca(example_isolates,
      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
      minimum = 10) # Recommended threshold: >=30
#> Warning: invalid microorganism code, NA generated

Piperacillin/tazobactam	Piperacillin/tazobactam + Gentamicin	Piperacillin/tazobactam + Tobramycin
70% (64.7-75.3%)	93.6% (92.1-95%)	89.8% (86.7-92.4%)

WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:

wisca(example_isolates,
      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
      syndromic_group = "ward",
      minimum = 10) # Recommended threshold: >=30
#> Warning: invalid microorganism code, NA generated

Syndromic Group	Piperacillin/tazobactam	Piperacillin/tazobactam + Gentamicin	Piperacillin/tazobactam + Tobramycin
Clinical	74.7% (68.7-80.4%)	93.6% (91.9-95.2%)	90.4% (86.9-93.1%)
ICU	56.9% (48.5-65.6%)	86.7% (83.2-89.7%)	83% (78.2-87.2%)
Outpatient	57.4% (46-68.9%)	76.6% (70.7-82.3%)	67.5% (57.2-77.3%)

For AMR surveillance, traditional antibiograms remain the right tool for tracking resistance per species over time:

antibiogram(example_isolates,
            mo_transform = "gramstain",
            antimicrobials = c("AMC", carbapenems(), "TZP"))
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)

Pathogen	Amoxicillin/clavulanic acid	Imipenem	Meropenem	Piperacillin/tazobactam
Gram-negative	76% (73-79%,N=726)	99% (98-100%,N=631)	100% (99-100%,N=626)	88% (85-91%,N=641)
Gram-positive	76% (74-79%,N=1138)	81% (75-85%,N=257)	77% (70-82%,N=203)	86% (82-89%,N=345)

Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species:

antibiogram(example_isolates,
            mo_transform = "gramstain",
            antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))

Pathogen	Piperacillin/tazobactam	Piperacillin/tazobactam + Gentamicin	Piperacillin/tazobactam + Tobramycin
Gram-negative	88% (85-91%,N=641)	99% (97-99%,N=691)	98% (97-99%,N=693)
Gram-positive	86% (82-89%,N=345)	98% (96-98%,N=1044)	95% (93-97%,N=550)

Like many other functions in this package, antibiogram() and wisca() come with support for 28 languages that are often detected automatically based on system language:

antibiogram(example_isolates,
            antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
            mo_transform = "gramstain",
            ab_transform = "name",
            language = "uk") # Ukrainian

Збудник	Гентаміцин	Тобраміцин	Ципрофлоксацин
Грамнегативні	96% (95-98%,N=684)	96% (94-97%,N=686)	91% (88-93%,N=684)
Грампозитивні	63% (60-66%,N=1170)	34% (31-38%,N=665)	77% (74-80%,N=724)

Interpreting and plotting MIC and SIR values

The AMR package allows interpretation of MIC and disk diffusion values based on CLSI and EUCAST. Moreover, the ggplot2 package is extended with new scale functions, to allow plotting of log2-distributed MIC values and SIR values.

library(ggplot2)
library(AMR)

# generate some random values
some_mic_values <- random_mic(size = 100)
some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
interpretation <- as.sir(some_mic_values,
                         guideline = "EUCAST 2024",
                         mo = "E. coli", # or any code or name resembling a known species
                         ab = "Cipro")   # or any code or name resembling an antibiotic

# create the plot
ggplot(data.frame(mic = some_mic_values,
                  group = some_groups,
                  sir = interpretation),
       aes(x = group, y = mic, colour = sir)) +
  theme_minimal() +
  geom_boxplot(fill = NA, colour = "grey30") +
  geom_jitter(width = 0.25) +
  
  # NEW scale function: plot MIC values to x, y, colour or fill
  scale_y_mic() +
  
  # NEW scale function: write out S/I/R in any of the 20 supported languages
  #                     and set colourblind-friendly colours
  scale_colour_sir()

Calculating resistance per group

For a manual approach, you can use the resistance or susceptibility() function:

example_isolates %>%
  # group by ward:
  group_by(ward) %>%
  # calculate AMR using resistance() for gentamicin and tobramycin
  # and get their 95% confidence intervals using sir_confidence_interval():
  summarise(across(c(GEN, TOB),
                   list(total_R = resistance,
                        conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
#> # A tibble: 3 × 5
#>   ward       GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
#>   <chr>            <dbl> <chr>              <dbl> <chr>       
#> 1 Clinical         0.229 0.205-0.254        0.315 0.284-0.347 
#> 2 ICU              0.290 0.253-0.33         0.400 0.353-0.449 
#> 3 Outpatient       0.2   0.131-0.285        0.368 0.254-0.493

Or use antimicrobial selectors to select a series of antibiotic columns:

library(AMR)
library(dplyr)

out <- example_isolates %>%
  # group by ward:
  group_by(ward) %>%
  # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
  summarise(across(c(aminoglycosides(), polymyxins()),
            resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN
#>   (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (whilst `minimum =
#> 30`).
out
#> # A tibble: 3 × 6
#>   ward         GEN   TOB   AMK   KAN   COL
#>   <chr>      <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical   0.229 0.315 0.626     1 0.780
#> 2 ICU        0.290 0.400 0.662     1 0.857
#> 3 Outpatient 0.2   0.368 0.605    NA 0.889

# transform the antibiotic columns to names:
out %>% set_ab_names()
#> # A tibble: 3 × 6
#>   ward       gentamicin tobramycin amikacin kanamycin colistin
#>   <chr>           <dbl>      <dbl>    <dbl>     <dbl>    <dbl>
#> 1 Clinical        0.229      0.315    0.626         1    0.780
#> 2 ICU             0.290      0.400    0.662         1    0.857
#> 3 Outpatient      0.2        0.368    0.605        NA    0.889

# transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc")
#> # A tibble: 3 × 6
#>   ward       J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
#>   <chr>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 Clinical     0.229   0.315   0.626       1   0.780
#> 2 ICU          0.290   0.400   0.662       1   0.857
#> 3 Outpatient   0.2     0.368   0.605      NA   0.889

What else can you do with this package?

This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:

• Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (manual)
• Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline (manual)
• Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records (manual)
• Determining first isolates to be used for AMR data analysis (manual)
• Calculating antimicrobial resistance (tutorial)
• Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial)
• Calculating (empirical) susceptibility of both mono therapy and combination therapies (tutorial)
• Apply AMR functions in predictive modelling (tutorial)
• Getting properties for any microorganism (like Gram stain, species, genus or family) (manual)
• Getting properties for any antimicrobial (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) (manual)
• Plotting antimicrobial resistance (tutorial)
• Applying EUCAST expert rules (manual)
• Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code (manual)
• Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code (manual)
• Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to SIR (link)
• Principal component analysis for AMR (tutorial)

Get this package

Latest official version

This package is available here on the official R network (CRAN). Install this package in R from CRAN by using the command:

install.packages("AMR")

It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.

Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest beta version.

Latest beta version

Please read our Developer Guideline here.

To install the latest and unpublished beta version:

install.packages("AMR", repos = "beta.amr-for-r.org")

# if this does not work, try to install directly from GitHub using the 'remotes' package:
remotes::install_github("msberends/AMR")

Get started

To find out how to conduct AMR data analysis, please continue reading here to get started or click a link in the ‘How to’ menu.

Partners

The initial development of this package was part of, related to, or made possible by the following non-profit organisations and initiatives:

Copyright

This R package is free, open-source software and licensed under the GNU General Public License v2.0 (GPL-2). In a nutshell, this means that this package:

• May be used for commercial purposes

• May be used for private purposes

• May not be used for patent purposes

• May be modified, although:

  • Modifications must be released under the same license when distributing the package
  • Changes made to the code must be documented

• May be distributed, although:

  • Source code must be made available when the package is distributed
  • A copy of the license and copyright notice must be included with the package.

• Comes with a LIMITATION of liability

• Comes with NO warranty